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Welcome to the Enterprise Agent Discussion Group

Enterprise Agents

Enterprise Agent Categories and Delivery Difficulty

A Unified Semantic Model for Enterprise Agents

What Anthropic Financial Services Reveals About General Enterprise Agent Architecture

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What Kind of Memory Do Enterprise Agents Need?

On Self-Evolution in Online AI Systems

On Agent Self-Evolution

Ask-Data Agents / Semantic Layer

Technical Paths and Trade-offs for Ask-Data Systems

Cube Core: A Semantic Layer Framework for Ask-Data Systems

Ask-Data Hasn't Failed—It Needs a Different Delivery Approach

Semantic-Native Ask-Data System Delivery Series

Semantic-Native Ask-Data System Delivery (1): Starting from Amazon Product Selection and Competitor Reviews

Semantic-Native Ask-Data System Delivery (2): From Public Data to an Analysis-Ready Warehouse

Semantic-Native Ask-Data System Delivery (3): The Unified Semantic Layer

Semantic-Native Ask-Data System Delivery (4): Constraining Natural-Language Questions into Controlled Cube Queries

Semantic-Native Ask-Data System Delivery (5): How One Semantic Layer Serves Agents, Dashboards, and ML Pipelines

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How to Do Regression Testing for Ask-Data Systems

From Demo to Usable: The Right Delivery Path for Enterprise Ask-Data

Ask-Data Deployment Discussions Analyzed from 472 Comments

Trusted Enterprise Data Foundations: Architecture and Vendor Landscape

How to Build a Trusted Enterprise Data Foundation in the Intelligent Era

Enterprise AI

What Does AI-Native Mean?

How Enterprises Should Embrace the Intelligent Era

How to Build a Trusted Enterprise Data Foundation in the Intelligent Era

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The New Engineer in the AI Era: What Capabilities Does an FDE Need?

What an Algorithm Engineer Job Post Says About Outdated Role Design

Strategic Misalignment in the AI Era Through Hiring Data

AI Engineering

Estimating LLM Inference Cost with Precision

VRAM Requirements for Training and Fine-Tuning Large Models

KV Quantization: The Cost-Saving Trick in LLM Inference

PagedAttention in Practice

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How to Think About Output Length in Large Models

The Complexity of Software Stacks for Domain-Specific Accelerators

How Many Tokens Does One Parameter Need for Training?

Hybrid Heterogeneous Compute Clusters in the LLM Era

Understanding vLLM's PagedAttention

AI Chips Explained: GPU, TPU, and Compute-in-Memory

How Large Models Actually Use Tools

RAG / Embeddings

Practical Challenges in Deploying RAG Systems

Progress in Embedding Models

How to Use Embedding Models Correctly

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RAG Deployment Discussions Analyzed from 1,091 Comments

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AI Foundations

The Evolution of LLM Architectures

Hugging Face Series

HF1: Transformers and Sentence-Transformers

HF2: Understanding Text Generation Parameters in Transformers

HF3: Beam Search and Sampling

HF4: The Role of the Transformers Library in the Hugging Face Ecosystem

HF5: Transformers and Inference Engines

HF6: PyTorch and Transformers

HF7: How Hugging Face Works

HF8: A Full-Spectrum Guide to Hugging Face

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Foundations 1: Understanding FFT

Foundations 2: Orthonormal Bases in Theory and Practice

Foundations 3: Proving the Universal Approximation Theorem

Foundations 4: From Fourier Representation to Universal Approximation

Foundations 5: Autoencoders and PCA Equivalence

Foundations 6: Between PCA and Universal Approximation

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Understanding Mixture-of-Experts Architectures

Understanding Emergence in Large Models

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Diffusion Models: Generative Art from Chaos to Order

An Overview of Reinforcement Learning

Classic Architectures and Operators in CV and Vision-Language Models

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TopicAsk-Data Agents / Semantic Layer

Published2026-05-01 00:56

WeChat account智能大时代

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